NDLI: Comparative Study: Common ANN and LS-SVM Exchange Rate Performance Prediction

Content Provider	IET Digital Library
Author	Sun, Ai Zhao, Tianyi Chen, Jungfang Chang, Juifang
Abstract	Due to the mathematical modeling principle deficiency, the data-driven neural network and support vector machine methods have become the powerful basic methods for the exchange rate prediction. Based on the analysis of the characteristics of exchange rate time series data, the exchange rate prediction performance of Artificial neural network (ANN) and Least squares-Support vector machine (LS-SVM) is explored. The parameter optimization method of the two-times training is proposed. The fundamental principle of LS-SVM prediction is analysed in detail. By virtue of daily, monthly and quarterly data of three currency exchange rates, the prediction performance of LS-SVM is examined. The comparison is made with ANN prediction results based on the same data in relevant literature review. According to the experimental result, LS-SVM has better short-term prediction performance, and it is superior to ANN in most cases in terms of prediction precision.
Starting Page	561
Ending Page	564
Page Count	4
ISSN	10224653
Volume Number	27
e-ISSN	20755597
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/27/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2018.01.003
Journal	Chinese Journal of Electronics
Publisher Date	2018-05-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	ANN Prediction Result Artificial Neural Network Currency Exchange Rates Data-driven Neural Network Exchange Rate Prediction Performance Exchange Rate Time Series Data Exchange Rates Financial Computing Financial Data Processing Interpolation And Function Approximation Knowledge Engineering Technique Least Squares Approximation Least Squares-support Vector Machine Method LS-SVM Prediction Neural Computing Technique Neural Nets Numerical Analysis Parameter Optimization Method Statistics Support Vector Machine Time Series
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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